Background: Multiple chronic conditions (MCC) contribute to functional disability in the general population although its role in predicting functional outcome (FO) among patients with stroke is not well understood. There is no universal agreement on the approach to measuring MCC in stroke, and findings have been mixed regarding MCC being an independent predictor for poststroke FO. Objectives: This review aims to summarize the findings of studies that have investigated the relationship between MCC and FO after ischemic stroke using a MCC index. Method: PubMed and Embase were systematically searched for studies conducted among ischemic stroke patients that have examined the adjusted association between prestroke MCC and FO. The quality of the included studies was appraised using a risk of bias (RoB) assessment checklist. A meta-analysis was performed for the association between MCC and FO using a random effects model to estimate the overall pooled ORs. Results: Twelve of the 18 studies included were hospital-based cohort studies, with a median RoB score of 4.75 points (range 1–9, higher scores for higher RoB). Studies predominantly used the Charlson Comorbidity Index (CCI), or the Modified CCI to measure MCC burden, and the modified Rankin scale to measure FO. Half of the studies reported a significant negative association between MCC and FO, which was also found by the meta-analysis with a pooled OR of 1.11 (95% CI 1.05–1.18). Conclusions: The current review supports that increased MCC is associated with worse poststroke FO although population-based studies of this association are lacking. Future research should aim to develop more refined measures of MCC that consider the severity and interactions of comorbid conditions reflective of the broader stroke population and to understand the relationship between MCC and poststroke FO with thorough adjustment for confounding factors.

Background: Multiple chronic conditions (MCC) contribute to functional disability in the general population although its role in predicting functional outcome (FO) among patients with stroke is not well understood. There is no universal agreement on the approach to measuring MCC in stroke, and findings have been mixed regarding MCC being an independent predictor for poststroke FO. Objectives: This review aims to summarize the findings of studies that have investigated the relationship between MCC and FO after ischemic stroke using a MCC index. Method: PubMed and Embase were systematically searched for studies conducted among ischemic stroke patients that have examined the adjusted association between prestroke MCC and FO. The quality of the included studies was appraised using a risk of bias (RoB) assessment checklist. A meta-analysis was performed for the association between MCC and FO using a random effects model to estimate the overall pooled ORs. Results: Twelve of the 18 studies included were hospital-based cohort studies, with a median RoB score of 4.75 points (range 1–9, higher scores for higher RoB). Studies predominantly used the Charlson Comorbidity Index (CCI), or the Modified CCI to measure MCC burden, and the modified Rankin scale to measure FO. Half of the studies reported a significant negative association between MCC and FO, which was also found by the meta-analysis with a pooled OR of 1.11 (95% CI 1.05–1.18). Conclusions: The current review supports that increased MCC is associated with worse poststroke FO although population-based studies of this association are lacking. Future research should aim to develop more refined measures of MCC that consider the severity and interactions of comorbid conditions reflective of the broader stroke population and to understand the relationship between MCC and poststroke FO with thorough adjustment for confounding factors.

Background: Stroke outcome studies often combine cases of intracerebral hemorrhage (ICH) and ischemic stroke (IS). These studies of mixed stroke typically ignore computed tomography (CT) findings for ICH cases, though the impact of omitting these traditional predictors of ICH mortality is unknown. We investigated the incremental impact of ICH CT findings on mortality prediction model performance. Methods: Cases of ICH and IS (2000–2003) were identified from the Brain Attack Surveillance in Corpus Christi (BASIC) project. Base models predicting 30-day mortality included demographics, stroke type, and clinical findings (National Institutes of Health Stroke Scale (NIHSS) +/– Glasgow Coma Scale (GCS)). The impact of adding CT data (volume, intraventricular hemorrhage, infratentorial location) was assessed with the area under the curve (AUC), unweighted sum of squared residuals (Ŝ), and integrated discrimination improvement (IDI). The model assessment was performed first for the mixed case of IS and ICH, and then repeated for ICH cases alone to determine whether any lack of improvement in model performance with CT data for mixed stroke type was due to IS cases naturally forming a larger proportion of the total sample than ICH. Results: A total of 1,256 cases were included (86% IS, 14% ICH). Thirty-day mortality was 16% overall (11% for IS; 43% for ICH). When both clinical scales (NIHSS and GCS) were included, none of the model performance measures showed improvement with the addition of CT findings whether considering IS and ICH together (ΔAUC: 0.002, 95% CI –0.01, 0.02; ΔŜ: –3.0, 95% CI –9.1, 2.6; IDI: 0.017, 95% CI –0.004, 0.05) or considering ICH cases alone (ΔAUC: 0.02, 95% CI –0.02, 0.08; ΔŜ: –2.0, 95% CI –9.7, 3.4; IDI 0.065, 95% CI –0.03, 0.21). If NIHSS was the only clinical scale included, there was still no improvement in AUC or Ŝ when CT findings were added for the sample with IS/ICH combined (ΔAUC: 0.005, 95% CI –0.01, 0.02; ΔŜ: –5.0, 95% CI –11.6, 1.0) or for ICH cases alone (ΔAUC: 0.05, 95% CI –0.002, 0.11; ΔŜ: –4.2, 95% CI –11.5, 2.3). However, IDI was improved when NIHSS was the only clinical scale for IS/ICH combined (IDI: 0.029, 95% CI 0.002, 0.065) and ICH alone (IDI: 0.12, 95% CI 0.005, 0.26). Conclusions: Excluding ICH CT findings had only minimal impact on mortality prediction model performance whether examining ICH and IS together or ICH alone. These findings have important implications for the design of clinical studies involving ICH patients.

Background: Stroke outcome studies often combine cases of intracerebral hemorrhage (ICH) and ischemic stroke (IS). These studies of mixed stroke typically ignore computed tomography (CT) findings for ICH cases, though the impact of omitting these traditional predictors of ICH mortality is unknown. We investigated the incremental impact of ICH CT findings on mortality prediction model performance. Methods: Cases of ICH and IS (2000–2003) were identified from the Brain Attack Surveillance in Corpus Christi (BASIC) project. Base models predicting 30-day mortality included demographics, stroke type, and clinical findings (National Institutes of Health Stroke Scale (NIHSS) +/– Glasgow Coma Scale (GCS)). The impact of adding CT data (volume, intraventricular hemorrhage, infratentorial location) was assessed with the area under the curve (AUC), unweighted sum of squared residuals (Ŝ), and integrated discrimination improvement (IDI). The model assessment was performed first for the mixed case of IS and ICH, and then repeated for ICH cases alone to determine whether any lack of improvement in model performance with CT data for mixed stroke type was due to IS cases naturally forming a larger proportion of the total sample than ICH. Results: A total of 1,256 cases were included (86% IS, 14% ICH). Thirty-day mortality was 16% overall (11% for IS; 43% for ICH). When both clinical scales (NIHSS and GCS) were included, none of the model performance measures showed improvement with the addition of CT findings whether considering IS and ICH together (ΔAUC: 0.002, 95% CI –0.01, 0.02; ΔŜ: –3.0, 95% CI –9.1, 2.6; IDI: 0.017, 95% CI –0.004, 0.05) or considering ICH cases alone (ΔAUC: 0.02, 95% CI –0.02, 0.08; ΔŜ: –2.0, 95% CI –9.7, 3.4; IDI 0.065, 95% CI –0.03, 0.21). If NIHSS was the only clinical scale included, there was still no improvement in AUC or Ŝ when CT findings were added for the sample with IS/ICH combined (ΔAUC: 0.005, 95% CI –0.01, 0.02; ΔŜ: –5.0, 95% CI –11.6, 1.0) or for ICH cases alone (ΔAUC: 0.05, 95% CI –0.002, 0.11; ΔŜ: –4.2, 95% CI –11.5, 2.3). However, IDI was improved when NIHSS was the only clinical scale for IS/ICH combined (IDI: 0.029, 95% CI 0.002, 0.065) and ICH alone (IDI: 0.12, 95% CI 0.005, 0.26). Conclusions: Excluding ICH CT findings had only minimal impact on mortality prediction model performance whether examining ICH and IS together or ICH alone. These findings have important implications for the design of clinical studies involving ICH patients.